Paper Info
Title
An Improved Block-Matching Algorithm Based On Chaotic Sine-Cosine Algorithm For Motion Estimation
Abstract
Motion estimation (ME) plays an important role in a video coding solution to achieve a low bit rate. The selection of the optimal motion vector (MV) has a significant impact on the quality of the compressed video. Block-matching (BM) algorithm is one of the widely accepted ME techniques to estimate the motion between the successive frames. In any BM technique, the motion vectors (MVs) are obtained for the current frame over a pre-defined search region in the previous frame by minimizing certain matching criterion. However, the computation of these matching criteria is highly expensive (in terms of the computational time). Hence, the block-based ME (BME) can be realized as an optimization problem which aims at finding the best-matched block within a specified search region. In this context, an improved block-matching technique is proposed that incorporates a chaotic-based sinecosine optimization algorithm along with a fitness approximation (FA) strategy. The proposed approach has been compared with several other BM techniques in terms of different parameters, namely, the peak-signal-to-noise-ratio (PSNR), PSNR degradation ratio (DPSNR), and the number of search points. The analysis of the results obtained demonstrates that the proposed method yields potential improvements over other competent schemes.
Year
DOI
Venue
2018
10.1007/978-3-030-01424-7_74
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT III
Keywords
Field
DocType
Block-matching, Optimization, Motion estimation, Sine-Cosine algorithm, Motion vector
Block-matching algorithm,Pattern recognition,Computer science,Coding (social sciences),Fitness approximation,Artificial intelligence,Motion estimation,Chaotic,Optimization problem,Motion vector,Computation
Conference
Volume
ISSN
Citations 
11141
0302-9743
0
PageRank 
References 
Authors
0.34
27
2
Name
Order
Citations
PageRank
Bodhisattva Dash152.10
Suvendu Rup2115.55